Das Herzzeitvolumen ist ein wichtiger Parameter zur Beurteilung der Hämodynamik. Trotz des Bedarfs umfangreichen Monitorings in der heutigen Hochleistungsmedizin hat sich bisher keine Methode etablieren können, die diese Größe zuverlässig auf kontinuierlicher Basis bestimmt. Die vorliegende Arbeit untersucht in einem Großtierhämodynamikmodell am Schwein die Möglichkeit, durch Verrechnungen des rechtsventrikulären Druckes (RVP) das korrespondierende Schlagvolumen (SV) zu bestimmen und dadurch eine neue Methode zur kontinuierlichen Schlagvolumen- respektive Herzzeitvolumenbestimmung zu entwickeln. Dazu wurden bei insgesamt 16 Tieren in einem computergestützten Meßsystem kontinuierlich neben anderen wesentlichen Fluß- und Druckparametern der RVP mittels piezoresistiver Druckmessung und das SV mittel Ultraschall-Transit-Time bestimmt. Schlagvolumenvariationen wurden durch akute Änderungen der kardialen Vor- und Nachlast sowie unter Bedingungen der Koronarischämie erreicht. So wurden die Atmungsparameter variiert, die Tiere atrial und ventrikulär frequenzmoduliert, sowie unter Applikation von Dobutamin untersucht. In einer Modifikation der Pulskonturmethode wurde die Fläche unter der RVP-Kurve während der Austreibungsphase als Schlagvolumen bestimmt (SVRVP). Diese Fläche wird von der Geraden mit den Schnittpunkten des RVP zu den Zeitpunkten des Maximums und des Minimums seiner ersten Ableitung (dP/dt) begrenzt. Die errechneten Werte für SVRVP wurden zu den per Ultraschall bestimmten SV-Werten in Korrelation gesetzt. Die Regressionsanalysen zwischen SVRVP und SV zeigten einen engen linearen Zusammenhang zwischen beiden Größen bei geringen Standardfehlern. Dies traf für alle Interventionen - jeweils für sich und im Zusammenhang - gleichermaßen zu. Somit erscheint durch die aufgezeigte Verrechnung des RVP eine kontinuierliche Herzzeitvolumenmessung möglich. Anwendungsmöglichkeiten dieses einfach anzuwendenden Verfahrens ergeben sich im Monitoring auf Intensivstationen sowie im perioperativen Bereich. In erster Linie aber eröffnen sich neue Wege in der ambulanten Diagnostik und Therapieüberwachung von Patienten mit chronischer Herzinsuffizienz. / Cardiac output is an important parameter of haemodynamics. Despite the need for extensive monitoring in todays hightech medicine a method that can detect this parameter reliably on a continous basis has not yet emerged. We tried to develop a new method on a haemodynamic pig model, to continously calculate the corresponding stroke volume (SV) from the right ventricular pressure curve (RVP) on the basis of the pulse contour method. Sixteen pigs were examined. RVP and SV, among other flow and pressure parameters, were continously monitored on a computer-based-system. RVP was measured by piezo-resistive pressure monitoring, SV was determined by the ultrasound-transit-time method. Variations of stroke volume were achieved by altering pre- and afterload and by inducing myocardial ischemia. The pigs were examined under varying parameters of respiration, atrial and ventricular stimulation, and application of dobutamine. In a modification of the pulse contour method the area under the RVP-curve during the ejection period was determined as stroke volume (SV_RVP). This area is limited by the straight line intersecting the RVP at the time of the maximum and minimum of its first derivative (dP/dt). The calculated data for SVRVP was correlated to the SV determined via the ultrasound-transit-time-method. The regression analysis of SV and SVRVP showed a close and linear relationship between the two parameters with a small standard error. This was true for all interventions. It is therefore possible to monitor cardiac output continously with the variation of the pulse contour method used here. This technique with little invasion may be used for monitoring on intensive care units and for the perioperative care. First of all it opens new ways in ambulatory diagnosis and optimizing medical therapy of patients with congestive heart failure.

The high specific strength and stiffness characteristics of composite materials such as carbon fiber-reinforced plastic (CFRP) allow a significant weight reduction of the structural machine components such as automobile driveshafts. But high material cost and rather low productivity of the established manufacturing processes (e.g., filament winding) often inhibit the use of CFRP components in a high-volume car series. In this paper, a novel composite driveshaft system based on a profiled CFRP tube is presented. This system is designed to be produced by a continuous pultrusion process to achieve a significant reduction of the manufacturing costs. A cost assessment study was conducted to quantify the benefit of the developed continuous manufacturing process. In comparison with the state-of-the-art filament winding process, a cost reduction of 36% for the composite shaft body can be obtained. Moreover, the proposed fiber layup processes – braiding and continuous winding – offer the potential to manipulate the reinforcement architecture to maximize material utilization without reducing the manufacturing efficiency. This potential is investigated and validated by experimental tests. A difference in the load bearing capacity of more than 100% between different reinforcing architectures is shown.

Visual place recognition is the task of finding same places in a set of database images for a given set of query images. This becomes particularly challenging for long-term applications when the environmental condition changes between or within the database and query set, e.g., from day to night. Visual place recognition in changing environments can be used if global position data like GPS is not available or very inaccurate, or for redundancy. It is required for tasks like loop closure detection in SLAM, candidate selection for global localization, or multi-robot/multi-session mapping and map merging. In contrast to pure image retrieval, visual place recognition can often build upon additional information and data for improvements in performance, runtime, or memory usage. This includes additional data-inherent knowledge about information that is contained in the image sets themselves because of the way they were recorded. Using data-inherent knowledge avoids the dependency on other sensors, which increases the generality of methods for an integration into many existing place recognition pipelines. This thesis focuses on the usage of additional data-inherent knowledge. After the discussion of basics about visual place recognition, the thesis gives a systematic overview of existing data-inherent knowledge and corresponding methods. Subsequently, the thesis concentrates on a deeper consideration and exploitation of four different types of additional data-inherent knowledge. This includes 1) sequences, i.e., the database and query set are recorded as spatio-temporal sequences so that consecutive images are also adjacent in the world, 2) knowledge of whether the environmental conditions within the database and query set are constant or continuously changing, 3) intra-database similarities between the database images, and 4) intra-query similarities between the query images. Except for sequences, all types have received only little attention in the literature so far. For the exploitation of knowledge about constant conditions within the database and query set (e.g., database: summer, query: winter), the thesis evaluates different descriptor standardization techniques. For the alternative scenario of continuous condition changes (e.g., database: sunny to rainy, query: sunny to cloudy), the thesis first investigates the qualitative and quantitative impact on the performance of image descriptors. It then proposes and evaluates four unsupervised learning methods, including our novel clustering-based descriptor standardization method K-STD and three PCA-based methods from the literature. To address the high computational effort of descriptor comparisons during place recognition, our novel method EPR for efficient place recognition is proposed. Given a query descriptor, EPR uses sequence information and intra-database similarities to identify nearly all matching descriptors in the database. For a structured combination of several sources of additional knowledge in a single graph, the thesis presents our novel graphical framework for place recognition. After the minimization of the graph's error with our proposed ICM-based optimization, the place recognition performance can be significantly improved. For an extensive experimental evaluation of all methods in this thesis and beyond, a benchmark for visual place recognition in changing environments is presented, which is composed of six datasets with thirty sequence combinations.